Staff Data Scientist – Sam’s Fraud Prevention

Walmart • Bentonville, AR

11d • $110,000 - $220,000 • Onsite

About The Position

We are seeking a Staff Data Scientist to join our Sam's Fraud Prevention team. In this role, you will be responsible for architecting and productionizing advanced AI-powered fraud decisioning systems. You will leverage cutting-edge technologies such as LLM orchestration, graph-derived embeddings, and ML-based risk scoring to aggregate signals, synthesize risk narratives, and generate automated recommendations. Your work will significantly reduce manual review dependency and decision latency. You will also design and deploy ML-driven agents for chargeback dispute automation, aiming to improve recovery rates and reduce operational overhead. A key aspect of this role involves building a unified cross-channel member profile to enable more precise fraud detection and reduce false positives. You will develop and productionize batch inference models to complement real-time pipelines and engineer a large-scale distributed fraud graph platform. Additionally, you will spearhead the development of an intelligent retail AI system for proactive theft prevention and establish feedback-driven fraud learning loops for continuous system improvement. This role requires owning the full ML lifecycle and partnering cross-functionally with various stakeholders to operationalize advanced fraud systems at scale.

Requirements

• coding in one of the following object-oriented programming languages: C++ or Python
• Machine Learning and Deep Learning models, including CNN, Neural Networks and Bayesian Techniques
• Statistics and Probability
• performing data analysis and data collection using Python
• identifying and applying metrics for measuring success and failure including F1, precision, recall and hypothesis testing
• generating appropriate graphical representations of data and model outcomes
• building scalable machine learning models for anomaly detection and Credit Risk
• Data Visualization in Python, Power BI or Tableau
• AI Ethics, Model Fairness/Bias Monitoring
• Data and featuring engineering
• Master’s degree or the equivalent in Analytics, Economics, Computer Science, or related field OR Bachelor’s degree or the equivalent in Analytics, Economics, Computer Science, or related field plus 2 years of experience in analytics or a related field
• any amount of graduate coursework, graduate research experience or experience with the required skills

Responsibilities

• Architect and productionize Agentic AI–powered fraud decisioning system leveraging LLM orchestration, graph-derived embeddings, and ML-based risk scoring to aggregate multi-system signals, synthesize structured risk narratives, and generate automated accept/reject recommendations
• Design and deploy an ML-driven chargeback dispute automation agent that leverages structured feature extraction, document intelligence models, and evidence-ranking algorithms to auto-generate bank-ready dispute letters
• Build a unified cross-channel member profile by linking in-club, dotcom, returns, and transactional activity to create a holistic fraud-aware customer view
• Develop and productionize an end-to-end batch inference model that complements the real-time ML pipeline by issuing delayed risk recommendations for high-risk transactions
• Engineer a large-scale distributed fraud graph platform leveraging GraphDB and GraphFrames to model multi-hop relationships across devices, payment instruments, addresses, and behavioral signals; implement Graph Neural Networks (GNNs) and unsupervised graph embeddings to detect coordinated fraud rings and synthetic identity clusters
• Spearhead development of an intelligent retail AI system combining computer vision signals, behavioral ML, and anomaly detection algorithms to proactively prevent in-club theft
• Establish feedback-driven fraud learning loops by integrating model outputs, manual review outcomes, and downstream signals into continuous system improvement
• Own full ML lifecycle across multiple initiatives — from feature engineering and model design to deployment, monitoring, and business impact measurement
• Partner cross-functionally with Engineering, Fraud Operations, Policy, and Retail stakeholders to operationalize advanced fraud systems at scale

What This Job Offers

• Job Type: Full-time
• Career Level: Senior